The CSNG Lab <https://csng.mff.cuni.cz/> at the Faculty of Mathematics and Physics at the Charles University is seeking a highly motivated *Postdoctoral Researcher* to join our team to work on *digital twin model of visual system. *Funded by the JUNIOR Post-Doc Fund <https://cuni.cz/UKEN-178.html>, this position offers an exciting opportunity to conduct cutting-edge research at the intersection of systems neuroscience, computational modeling and AI. *Project description:* Modern deep-learning techniques have transformed visual neuroscience by substantially improving the ability of models to predict cortical neuron responses to unseen visual stimuli. However, current deep-learning methods have two major shortcomings. First, they focus on predicting only the average neural response, failing to capture the fine temporal dynamics generated within recurrent neural populations. Second, these models rely on standard "off-the-shelf" architectures optimized for efficient training rather than reflecting the biological substrate under study. As a result, they function as black boxes, making it difficult to interpret the learned representations in terms of how visual processing is organized and implemented in biological neural circuits. These limitations hinder the ability of deep-learning models to provide meaningful insights into the principles governing vision and translate them to clinical applications such as brain-machine-interface systems. To address these challenges, in this project *we will develop novel modular, multi-layer recurrent neural **network (RNN) architectures that directly mirror the architecture of the primary visual cortex.* Our models will establish a one-to-one mapping between individual neurons at different stages of the visual pathway and their artificial counterparts. They will explicitly incorporate functionally specific lateral recurrent interactions, excitatory and inhibitory neuronal classes, complex single-neuron transfer functions with adaptive mechanisms, synaptic depression, and others. We will first train our new RNNs on synthetic data generated by a state-of-the-art biologically realistic recurrent spiking model of the primary visual cortex developed in our group. After we establish the proof-of-concept on the synthetic data, we will translate our models to publicly available mouse and macaque data, as well as additional data from our experimental collaborators. *What do we offer:* We are the Computational Systems Neuroscience Group (CSNG) <https://csng.mff.cuni.cz/> based at the Faculty of Mathematics and Physics of Charles University, Prague. The main goal of our group is to identify computations implemented in the neural system that underlies sensory perception, as well as applying this knowledge to the design of stimulation protocols for visual prosthetic systems. To that end, we build models of visual systems at various levels of abstraction using a variety of computational techniques including, but not limited to, machine learning and large-scale biologically plausible spiking neural network simulations. The position is *fully funded for 2 years, *and comes with a salary equivalent to ~2400 EUR/month. We offer a dynamic international working environment and collaborations with world-leading experimental labs (Stanford, University of Pennsylvania, Institute de la Vision Paris etc.). *Candidate profile:* Strong background in modern machine learning techniques. Prior experience with training recurrent neural networks, and neuroscience or related disciplines is an advantage, but not strict requirement. Interested candidates should *contact Dr. Ján Antolík* ( jan.antolik@mff.cuni.cz) with their CV. *Deadline 5th August 2025.*